Artificial intelligence and machine learning have captured a lot of attention in recent years but they’re part of a much larger subset of technologies, called intelligent automation.
At their core, these technologies share the aim of automating thought – by analysing large amounts of data – but they output these insights in different ways.
In a recent podcast, Vertical Leap’s Managing Director, Chris Pitt, and Head of Services, Lee Wilson, discuss how intelligent automation can help large businesses scale their marketing efforts. Here is an except below and you can listen to the full podcast here.
The Search Marketing Podcast:
Q: Chris, what is intelligent automation and what role does it play in search?
“There are different subsets of intelligent automation and the first one is robotic process automation, RPA. This is about all the things that we do on a daily basis that are boring, labour-intensive or fairly technical, but consistently technical. And because of that consistency it’s stuff that we don’t do very well naturally ourselves, as humans.
“So RPA is not clever in the sense that it’s doing any thinking but what it is doing is taking away those manual repetitive processes and automating them.
“An example in search might be link analysis or data collection; or it might be looking for technical fixes on your website – lots of things that we do on a daily basis that don’t necessarily need any creativity or freeform thinking.
Q: Would you say intelligent automation is important for big brands scaling their search marketing strategies?
“Yes, it totally is – not just from a marketing perspective but across the whole business. There are tasks people do every day in businesses that could be done consistently better by technology. That doesn’t mean we should be replacing people. What it means is we free them up to do more of the tasks that the technology can’t do, such as strategy or implementation.
“I think that’s the key, for search generally, to shorten the distance to results, reduce the amount of ‘people time’ required to do the mundane things and free up more time to go a bit deeper which means we can be a bit more creative – and the technology can go deeper and wider than people can so it can identify more things for us to do. It achieves much greater scale.
Q: Lee, how does this time-saving aspect benefit your clients?
“What I like is the freeing-up of highly-skilled experts to focus on more of the creative, unique and experiential actions that are fundamentally human.
“It means that we can deliver more exciting and impactful marketing campaigns, but it also means that people have more time to nurture customer relationships and truly understand the businesses that they’re working with, the people that they’re working with, and to provide increasingly tailored and valuable search marketing solutions.”
Q: And this helps you scale technical SEO for larger brands?
“That’s exactly right and there are a variety of examples. Take citations [a reference to a business online that includes the business name, address, and phone number] as an example. When you think about a simple task like building citations, if you’re working with a company that’s got hundreds of locations, even if you only build one citation per location (which wouldn’t really work), you’d be building loads of citations on a monthly basis.
“So you can see how quickly the amount of human involvement can build up and how much resource is wasted just by doing a very monotonous, repetitive task that may well end up with errors because humans aren’t suited for that type of activity.
“But when you automate this kind of activity using intelligent automation, you can achieve more, at scale, with more efficiency, and all whilst the actual people can be focussing on implementation, creativity and strategy.
Q: Chris, you talk about intelligent automation being a stack of technologies – what does that mean?
“We talked about RPA and that’s the first stack, automating repetitive tasks.
“Then you’ve got analysis and insights and when I say this I’m talking about the things that we typically look for – the ‘known-knowns’. We know we’re looking for something, we know what we’re looking for and we probably look for it quite often – so it only makes sense to automate this process.
“And the last part of the stack is the part that everyone’s been talking about, which is AI and machine learning. This helps us find the ‘unknown knowns’ – the stuff we know that we don’t know about but we should probably find out, and this is where machine learning plays a real key part in looking at data right across the spectrum, much deeper than we could without automated intelligence.
“So, at the bottom of the stack, you’ve got robotic processing automation, which is automating everything that we do that could be automated and then you’ve got data insights and data science telling us where we are now and where we’ve got to go.”